Parkinson's Disease

Use of pimavanserin appears to be more effective than atypical antipsychotic agents in reducing mortality among Parkinson's disease patients dur...

“Pimavanserin, a serotonin 5-HT2 antagonist, is indicated for treatment of hallucinations and delusions associated with Parkinson’s disease psychosis,” the authors said. The mean age of pimavanserin users was approximately 78 years; 45 percent were female. However, such benefit is seen only in community-dwelling patients and not in nursing home residents.

“Parkinson's disease diagnosis relies on motor symptoms like tremors and stiffness, however, those symptoms tend to appear several years after the onset of the ...

"With [..]training, the algorithm was [..] able to detect complex patterns that identify people who have Parkinson's from those who don't." This can motivate a larger role for AI in the future or medicine." The researcher's AI device looks like a home WiFi router and works by analyzing the radio waves that bounce off people while they sleep, without any physical contact. "AI can help doctors and medical professionals extract new insights from standard physiological signals like breathing, heart rhythms, electrical activity, gait or walking patterns," said Katabi. Katabi published a new [study](https://www.nature.com/articles/s41591-022-01932-x) that looks at using artificial intelligence (AI) to analyze breathing patterns to aid in the early detection of Parkinson's before motor symptoms develop. “But, no physician today can detect Parkinson's or assess its severity merely from breathing.”

Researchers develop an artificial intelligence-based model to detect Parkinson's disease and track its progression from nocturnal breathing signals.

A strong correlation between the AI models' severity prediction and MDS-UPDRS was observed, thus indicating that the model captured PD disease severity well. Combining several nights for each subject increased the sensitivity and specificity of PD diagnosis to 100% for both PD and control subjects. For nights measured using a wireless dataset, the model reached an AUC of 0.906 with a sensitivity and specificity of 86.23% and 82.83%, respectively. The AI-based system described in the current study served as a promising diagnostic and progression digital biomarker for PD. Therefore, the researchers used the median PD score for each subject as the final diagnosis result. Taken together, the combined study data included 11,964 nights with over 120,000 hours of nocturnal breathing signals from 757 PD patients. The AI model diagnosed PD from one night of nocturnal breathing, which was presented as the receiver operating characteristic (ROC) curve. The control group consisted of 6,914 subjects, 30% of whom were women with a mean age of 66.2 years. Cross-institution prediction was determined by training and testing the model on data from different medical centers. The test-retest reliability was determined by averaging the prediction across consecutive nights within a pre-specified timeframe. The model learned the auxiliary task of predicting each subject's quantitative electroencephalogram (qEEG) from nocturnal breathing. The first method required that the test subject wore a breathing belt on the chest or abdomen to track breathing signals overnight.

The new system could become a vital tool for helping scientists and drug companies develop better treatments for the severe and incurable disease of the ...

It would collect a night’s worth of breathing data and transmit it to the doctor’s office for analysis. Beck said such a device would make it far easier for doctors to monitor Parkinson’s patients. The breathing data can be obtained with a belt that’s wrapped around a patient’s chest to detect expansion and contraction of the lungs. It uses software to filter out all other extraneous information, until only the breathing data remains. Working with MIT students as well as researchers at Rutgers University, the University of Rochester Medical Center, the Mayo Clinic, Massachusetts General Hospital, and Boston University, they collected breathing data from people as they slept. Katabi and her team decided to see if breathing patterns could reveal the disease.

The tool uses a series of connected algorithms that can assess if someone has PD from their nocturnal breathing - News - PharmaTimes.

The breathing signal is then fed to the neural network to assess Parkinson’s in a passive manner. This motivated us to consider the potential of detecting the disease from one’s breathing without looking at movements. PD is also notoriously difficult to diagnose as it relies primarily on the appearance of motor symptoms such as tremors, stiffness and slowness, but these symptoms often appear several years after the disease onset.

An MIT-developed device can detect the presence and severity of Parkinson's disease through patients' breathing patterns.

Early stage Parkinson's Disease can now be detected by a medtech device that measures a patient's nocturnal breathing patterns at home.

It’s also true that at the moment, there is no cure for Parkinson’s Disease – though treatments exist to mitigate symptoms and allow patients a better quality of life. The assessment of breathing patterns as a diagnostic condition of Parkinson’s Disease is not a wild stab in the dark. But as with many such devices in modern medtech, such as at-home detectors for atrial fibrillation, high blood pressure, and diabetes, the likelihood is that the detector will become commercialized and available as a ‘just in case’ piece of technology in the relatively short term. The AI was trained using several large datasets of patient and non-patient experiences, both in terms of sleep breathing and indicators for various diseases, particularly Parkinson’s Disease. To develop this non-invasive Parkinson’s detector, the team created a device that emits radio signals, analyzes their reflections off the surrounding environment, and extracts the subject’s breathing patterns, without any bodily contact. It’s a disease that has no significant biomarkers to announce its beginning or mark its progress until the tell-tale symptoms are observed, and even then, things like tremors and stiffness can frequently be mis-attributed to other causes.

An MIT-developed device with the appearance of a Wi-Fi router uses a neural network to discern the presence and severity of one of the fastest-growing ...

The analogy I like to draw [of current Parkinson’s assessments] is a street lamp at night, and what we see from the street lamp is a very small segment … [Ray Dorsey](https://www.urmc.rochester.edu/people/26764214-earl-ray-dorsey), a professor of neurology at the University of Rochester and Parkinson’s specialist who co-authored the paper. “A relationship between Parkinson’s and breathing was noted as early as 1817, in the work of Dr. Katabi notes that the study has important implications for Parkinson’s drug development and clinical care. This motivated us to consider the potential of detecting the disease from one’s breathing without looking at movements,” Katabi says. The MIT researchers demonstrated that the artificial intelligence assessment of Parkinson's can be done every night at home while the person is asleep and without touching their body.

The tool has the potential to improve Parkinson's diagnosis, which currently relies on the appearance of motor symptoms.

“In terms of drug development, the results can enable clinical trials with a significantly shorter duration and fewer participants, ultimately accelerating the development of new therapies,” she noted. This motivated us to consider the potential of detecting the disease from one’s breathing without looking at movements,” said Katabi, senior author of the paper, But these methods are both invasive and require patients to have access to specialized medical centers, making them impractical for the iterative early testing needed for early diagnosis. “A relationship between Parkinson’s and breathing was noted as early as 1817, in the work of Dr. The neural network, which was trained by MIT PhD student Yuzhe Yang and postdoc Yuan Yuan, can also diagnose the severity of the disease and track disease progression. The tool has the potential to significantly improve the diagnosis of this fast-growing neurological disease, which currently relies on the appearance of motor symptoms such as tremors, stiffness, and slowness that only become apparent several years after disease onset.

“A relationship between Parkinson's and breathing was noted as early as 1817, in the work of Dr. James Parkinson,” Dina Katabi, PhD, Thuan and Nicole Pham ...

With the wireless signal, researchers reported an AUC of 0.906 with a sensitivity of 86.23% (95% CI, 84.08-88.13) and specificity of 82.83% (95% CI, 79.94-85.40). “In terms of drug development, the results can enable clinical trials with a significantly shorter duration and fewer participants, ultimately accelerating the development of new therapies,” Katabi said in the release. “In terms of clinical care, the approach can help in the assessment of Parkinson’s patients in traditionally underserved communities, including those who live in rural areas and those with difficulty leaving home due to limited mobility or cognitive impairment.” Katabi and colleagues evaluated the AI model using a dataset of 7,671 individuals from several sources, including the Mayo Clinic, Massachusetts General Hospital sleep lab and observational clinical trials. Published Aug. “This motivated us to consider the potential of detecting the disease from one’s breathing without looking at movements.